1. Field of the Invention
The invention relates to a lightweight application classification scheme. In particular, the invention applies supervised machine learning techniques to the problem of network traffic classification for identifying causal applications of traffic flows in the management of network services.
2. Background of the Related Art
The continual growth and evolution of applications, hosts, and networks have been the hallmark of the Internet. This hallmark is expected to be typical of the next generation Internet. On the one hand, such a high level of continuous development has been a challenge for network management. On the other hand, network monitoring and measurement have been widely used in understanding how well the networks performs and ensuring certain levels of Quality of Service (QoS) specified in the Service Level Agreements (SLAs) with customers.
Despite a wide range of work on this in the past, the classification of network traffic continues to be a challenge. See, for example: In particular, network traffic classification is a complicated multi-factor system involving the mutual interaction of a range of networks, hosts, applications and protocols. Furthermore, there are harsh requirements on system performance and robustness before this class of methodology can be implemented and deployed for practical applications.
Traffic application classification is an essential step in the network management process to provide high availability of network services. However, network management has seen limited use of traffic classification because of the significant overhead of existing techniques. Accordingly, it would be beneficial to provide a lightweight, i.e., low overhead traffic classification scheme based on readily available records.